Research Awards/Grants (Current)

Min Kyung Lee

Chandra Bhat (University of Texas at Austin) and Yasser Shoukry (University of California-Irvine)

National Science Foundation (NSF)

06/01/2023 to 05/31/2027

The collaborative award is $2,000,000 over the project period. The School of Information portion of the award is $1,054,998. 

SCC-IRG Track 1: Community-Driven Design of Fair, Urban Air Mobility Transportation Management Systems

Urban Air Mobility (UAM) envisions integrating the skyscape into the transportation network and encompasses services such as delivery drones, on-demand shared mobility by Vertical-Take Off and Landing (VTOL) aircraft for intra-city passenger trips, and, in the longer run, electric and autonomous VTOLs. This possible modal alternative provides a safe, reliable, and environmentally sound option to reduce surface-level congestion. Nevertheless, the history of transportation infrastructure development shows that it is imperative to design transportation infrastructures with the community to find the best balance between these sociotechnical requirements. Much research shows that the design of transportation systems has a long-lasting, often discriminatory effect that reinforces existing socio-economic inequality. As UAM is being developed as a new transportation mode, we are at an opportune moment to design its infrastructure to provide effective and equitable air mobility for all, avoiding our past mistakes. This project will focus on understanding the preferences, attitudes, and concerns of all stakeholders of UAM, including the potential users of UAM, the general public in different communities who may be positively and/or adversely affected by UAM, policymakers, and city planners. The knowledge elicited from the stakeholders will guide the design of an open-source Computer Aided Planning tool that policy-makers and urban planners can use to design UAM infrastructure that accommodates communities? priorities and enables transportation equity. While the timeline for UAM may be in the future, its deployment may entail significant future investment in infrastructure which makes inclusion of equity considerations and early community engagement critical.

We propose a ''Community-in-the-Loop Integrative Framework for Fair and Equitable Urban Air Mobility (UAM) Infrastructure Design''. Our integrative framework will develop methods to engage with key stakeholders to address significant socio-technical challenges, including (a) understanding the community preferences and desiderata in terms of necessary considerations for equitable mobility, (b) developing novel machine learning techniques to generate design options that optimize for community desiderata efficiently and (c) devising community-driven evaluative measures and trade-off decision mechanisms. We address these challenges by drawing from urban and transportation engineering, aerospace, and computer and information sciences. The final product of our framework is an open-source Computer Aided Planning tool called VertiCAP. VertiCAP will be equipped with novel machine learning-based algorithms to navigate complex design space options, including long-term decisions (i.e., allocation of UAM airports, also known as vertiports), medium-term decisions (i.e., design of air space), and short-term decisions (i.e., air-traffic control). We will establish a ''community council'' representing different stakeholders. Through continuous interactions with the community council, we will evaluate and demonstrate the effectiveness of the developed VertiCAP tool in the City of Austin, TX and Southern California.

Ying Ding

led by Yifan Peng Weill Cornell Medicine

National Institutes of Health (NIH)

08/01/2023 to 04/30/2028

The collaborative award is $712,024 over the project period. The School of Information portion of the award is $333,944.

Closing the loop with an automatic referral population and summarization system

In the United States, more than a third of patients are referred to a specialist each year, and specialist visits constitute more than half of outpatient visits. Even though all physicians highly value communication between primary care providers (PCPs) and specialists, both PCPs and specialists cite the lack of effective information transfer as one of the most significant problems in the referral process. Therefore, it is critical to investigate a new method to improve communication during care transitions. With their ubiquitous use, it is recognized that electronic health records (EHRs) should ensure a seamless flow of information across healthcare systems to improve the referral process. But, a lack of accessible and relevant information in the referral process remains a pressing problem. Recently, emerging deep learning (DL) and natural language processing (NLP) methods have been successfully applied in extracting pertinent information from EHRs and generating text summarization to improve care quality and patient outcomes. However, existing technologies cannot be applied to process heterogeneous data from EHRs and create high-quality clinical summaries for communicating a reason for referral. Responding to PA-20-185, this project will develop and validate a novel informatics framework to collect and synthesize longitudinal, multimodal EHR data for automatic referral form generation and summarization. While the referring provider and specialist can be any type of provider for any condition, the focus in this application has been on headache for primary care, because it is an extremely common symptom and affects people of all ages, races, and socioeconomic statuses. More importantly, relevant information needed for headache referrals has been defined in local and national evidence-based practice guidelines. Therefore, a health information technology solution to make these data accessible will empower communication between PCPs and specialists, which can improve the care of millions of patients suffering from disabling headache disorders. Based on our preliminary data and our experience with an interdisciplinary team of data scientists and physicians, we plan to execute specific aims: 1) Convert text-based guidelines into a standards-based algorithm for electronic implementation; 2) develop models to automatically populate data from EHR and clinical notes to fill the referral form; 3) create a framework to summarize the longitudinal clinical notes to fill out the referral form; and 4) develop and validate the headache referral system with a user-centered design approach. The research proposed in this project is novel and innovative because it will produce and rigorously test new solutions to improve the communication between health professionals to ensure that safe, high-quality care is provided and care continuity is maintained. The success of this project will (1) fill important gaps in our knowledge of understanding the types of information exchange that will optimize patient care during transitions and (2) provide evidence-based solutions to enable the exchange.

Min Kyung Lee

Carin Håkansta (Karolinska Institutet)

Karolinska Institutet

07/01/2023 to 12/31/2025

The School of Information allocation from this collaborative award is $28,381.

ALGOSH: Algorithmic management at work - challenges, opportunities, and strategies for occupational safety and health and wellbeing

Algorithms are at the forefront of a transformative shift in the World of Work, profoundly influencing work dynamics, organizational structures, and the work environment. Despite their profound impact, a substantial knowledge gap exists concerning algorithmic management (AM) and its repercussions on occupational safety, health, and wellbeing. This gap is particularly pronounced in non-platform work settings, where AM's prevalence is growing.

As the use of AM continues to expand across various economic sectors, it is imperative to investigate its effects on the wellbeing of workers. The overarching objective of the ALGOSH research program is to enhance our understanding of AM in non-platform sectors and its impact on the health, safety, and wellbeing of workers. Moreover, it aims to develop tools and strategies to mitigate associated risks. The three research aims of ALGOSH are:

  • Facilitating the development of a standard for measurement of algorithmic management at work and related risks for health, safety and well-being.
  • Increasing knowledge about the effects of algorithmic management on workers’ health, safety, and well-being.
  • Investigating the balance of interests related to the control of algorithms in different legal contexts regarding occupational health and safety (OSH).

To accomplish this mission, an international and interdisciplinary consortium of researchers has been assembled. For our research to have maximum societal impact, the program also has a strong stakeholder involvement and support from trade unions, business organizations, international bodies, and government agencies. Their collective efforts will examine, discuss, and assess the opportunities and challenges posed by algorithmic management, fostering a safer and healthier work environment for all. The program applies multiple methods including quantitative, qualitative, literature reviews and participatory research. 

Kayla Booth

The Andrew W. Mellon Foundation

11/01/2021 to 10/31/2024

The award is $700,772 over the project period. 

Summer Institutes for Advanced Study in the Information Sciences

The iSchool Inclusion Institute (i3) is an undergraduate research and leadership development program that prepares students from underrepresented populations for graduate study and careers in the information sciences. Only 25 students from across the country are selected each year to become i3 Scholars. Those students undertake a yearlong experience that includes two summer institutes hosted by the University of Texas at Austin’s iSchool and a research project spanning the year. i3 prepares students for the rigors of graduate study and serves as a pipeline for i3 Scholars into internationally recognized information schools—the iSchools. Most importantly, i3 empowers students to create change and make an impact on the people around them.

Ahmer Arif

National Science Foundation (NSF)

10/01/2022 to 09/30/2024

The collaborative award is $5,000,000 over the project period. The School of Information portion of the award is $1,368,142

NSF Convergence Accelerator Track F: Co-designing for Trust: Reimagining Online Information Literacies with Underserved Communities

In 2011, the National Science Foundation began requiring that all funded projects provide data management
plans (DMPs) to ensure that project data, computer codes, and methodological procedures were available to other
scientists for future use. However, the extent to which these data management requirements have resulted in more and
better use of project data remains an open question. This project thus investigates the National Science Foundation's
DMP mandate as a national science policy and examines the broad impacts of this policy across a strategic sample of five
disciplines funded by the National Science Foundation. It considers the organization and structure of DMPs across fields,
the institutions involved in data sharing, data preservation practices, the extent to which DMPs enable others to use
secondary project data, and the kinds of data governance and preservation practices that ensure that data are sustained
and accessible. Systematic investigation of the impact of DMPs and data sharing cultures across fields will assist funding
agencies and research scientists working to produce reproducible and open science by identifying barriers to data
archiving, sharing, and access. The principal investigators will use project findings to develop data governance guidelines
for information professionals working with scientific data and to articulate best practices for scientific communities
using DMPs for data management.

This project aims to enhance understanding of the role data management plans (DMPs) play in shaping data life-cycles.
It does so by examining DMPs across five fields funded by the National Science Foundation to understand data practices,
archiving and access issues, the infrastructures that support data sharing and reuse, and the extent to which project
data are later used by other researchers. In phase I, the investigators will gather a strategic sample of DMPs
representing a wide range of data types and data retention practices from different scientific fields. Phase II consists of
forensic data analysis of a subset of DMPs to discover what has become of project data. Phase III develops detailed case
studies of research project data life-cycles and data afterlives with qualitative interviews and archival documentary
analysis to help develop best practices for sustainable data preservation, access, and sharing. Phase IV will translate
findings into data governance recommendations for stakeholders. The project thus contributes to research about
contemporary studies of scientific data production and circulation while assessing the effect of DMPs as a national
science policy initiative affecting data management practices in different scientific communities. The comparative
research design and mixed methods enables theory building about cross-disciplinary data practices and data cultures
across fields and advances knowledge within data studies, information management studies, and science and
technology studies.